中文摘要：

粮情智能测控系统是保证粮食储藏安全的关键措施之一.针对目前粮情智能测控系统中单一传感器存在测量不足、粮情安全状态难以判断且无法直接检测等问题,研究了一种基于粒子群优化最小二乘支持向量机的粮情安全状态数据融合方法.以粮情数据为研究对象,以粮情安全状态检测为目的,构建基于最小二乘支持向量机的数据融合模型,并采用粒子群优化算法对最小二乘支持向量机的关键参数进行寻优,获取最佳的数据融合结果.根据广西某粮库提供的数据进行实例分析,数据融合结果与实际值误差较小,均方误差为0.06,结果表明,该方法在粮情安全状态的检测上表现出优越的性能,具有科学性和可行性,提高了粮情智能测控系统的准确性和可靠性.

英文摘要：

The intelligent measurement and control system of grain situation is one of the key measures to ensure the safety of food storage.In view of the present grain situation intelligent measurement and control system with a single sensor,which has problems with measurement inadequacy,safety state of grain situation is difficult to be judged and can＇ t be directly detected,a data fusion method of grain situation safety status based on particle swarm optimization least squares support vector machine is studied.With grain situation data as the research object,the safety state of grain situation detection as the goal,a data fusion model based on least squares support vector machine is built.By using the particle swarm optimization calculation method to optimize the key parameters of least squares support vector machine,the best data fusion result is obtained.The error between the result of data fusion and the practical value is rather small through analyzing the data supported by a granary in Guangxi,the MSE reaches 0.06.Research result shows that this method shows superior performance in testing the safety status of grain situation,and has the scientific nature and feasibility,and can improve the accuracy and reliability of the intelligent measurement and control system of grain situation.